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Three satellite precipitation datasets--CMORPH, PERSIANN-CDR, and GPCP--from the NOAA/ Climate Data Record program were evaluated in their ability to capture seasonal differences in precipitation for the period 2007-18 over the conterminous United States. Data from the in situ U.S. Climate Reference Network (USCRN) provided reference precipitation measurements and collocated atmospheric conditions (temperature) at the daily scale. Satellite precipitation products' (SPP) performance with respect to cold season precipitation was compared to warm season and full-year analysis for benchmarking purposes. Considering an ensemble of typical performance metrics including accuracy, false alarm ratio, probability of detection, probability of false detection, and the Kling--Gupta efficiency (KGE) that combines correlation, bias, and variability, we found that the three SPPs displayed better performances during the warm season than during the cold season. Among the three datasets, CMORPH displayed better performance--smaller bias, higher correlation, and a better KGE score--than the two other SPPs on an annual basis and during the warm season. During the cold season, CMORPH showed the worst performance at higher latitudes over areas experiencing recurring snow or frozen and mixed precipitation. CMORPH's performances were further degraded compared to PERSIANN-CDR and GPCP when considering freezing temperatures (T < 08C) due to the inability to microwave sensors to retrieve precipitation over snow-covered surface. However, for cold rainfall events detected simultaneously by the satellite and the USCRN stations (i.e., conditional case), CMORPH performance noticeably improved but remained inferior to the two other datasets. The quantification of seasonal precipitation errors and biases for three satellite precipitation datasets presented in this work provides an objective basis for the improvement of rainfall retrieval algorithms of the next generation of satellite precipitation products. [ABSTRACT FROM AUTHOR]

This article reviews how precipitation microphysics processes are observed in dual-polarization radar observations. These so-called "fingerprints" of precipitation processes are observed as vertical gradients in radar observables. Fingerprints of rain processes are first reviewed, followed by processes involving snow and ice. Then, emerging research is introduced, which includes more quantitative analysis of these dual-polarization radar fingerprints to obtain microphysics model parameters and microphysical process rates. New results based on a detailed rain shaft bin microphysical model are presented, and we conclude with an outlook of potentially fruitful future research directions. [ABSTRACT FROM AUTHOR]

Three satellite gridded daily precipitation datasets—PERSIANN-CDR, GPCP, and CMORPH—that are part of the NOAA/Climate Data Record (CDR) program are evaluated in this work. The three satellite precipitation products (SPPs) are analyzed over their entire period of record, ranging from over 20 years to over 35 years. The products intercomparisons are performed at various temporal (daily to annual) resolutions and for different spatial domains in order to provide a detailed assessment of each SPP strengths and weaknesses. This evaluation includes comparison with in situ datasets from the Global Historical Climatology Network (GHCN-Daily) and the U.S. Climate Reference Network (USCRN). While the three SPPs exhibited comparable annual average precipitation, significant differences were found with respect to the occurrence and the distribution of daily rainfall events, particularly in the low and high rainfall rate ranges. Using USCRN stations over CONUS, results indicated that CMORPH performed consistently better than GPCP and PERSIANN-CDR for the usual metrics used for SPP evaluation (bias, correlation, accuracy, probability of detection, and false alarm ratio, among others). All SPPs were found to underestimate extreme rainfall (i.e., above the 90th percentile) from about −20% for CMORPH to −50% for PERSIANN-CDR. Those differences in performance indicate that the use of each SPP has to be considered with respect to the application envisioned, from the long-term qualitative analysis of hydroclimatological properties to the quantification of daily extreme events, for example. In that regard, the three satellite precipitation CDRs constitute a unique portfolio that can be used for various long-term climatological and hydrological applications. [ABSTRACT FROM AUTHOR]

The authors evaluate the contribution of tropical cyclones (TCs) to daily precipitation extremes over land for TC-active regions around the world. From 1998 to 2012, data from the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA 3B42) showed that TCs account for an average of 3.5% ± 1% of the total number of rainy days over land areas experiencing cyclonic activity regardless of the basin considered. TC days represent between 13% and 31% of daily extremes above 4 in. day−1, but can account locally for the large majority (>70%) or almost all (≈100%) of extreme rainfall even over higher-latitude areas marginally affected by cyclonic activity. Moreover, regardless of the TC basin, TC-related extremes occur preferably later in the TC season after the peak of cyclonic activity. [ABSTRACT FROM AUTHOR]

The National Centers for Environmental Prediction (NCEP) stage IV quantitative precipitation estimates (QPEs) are used in many studies for intercomparisons including those for satellite QPEs. An overview of the National Weather Service precipitation processing system is provided here so as to set the stage IV product in context and to provide users with some knowledge as to how it is developed. Then, an assessment of the stage IV product over the period 2002-12 is provided. The assessment shows that the stage IV product can be useful for conditional comparisons of moderate-to-heavy rainfall for select seasons and locations. When evaluating the product at the daily scale, there are many discontinuities due to the operational processing at the radar site as well as discontinuities due to the merging of data from different River Forecast Centers (RFCs) that use much different processing algorithms for generating their precipitation estimates. An assessment of the daily precipitation estimates is provided based on the cumulative distribution function for all of the daily estimates for each RFC by season. In addition it is found that the hourly estimates at certain RFCs suffer from lack of manual quality control and caution should be used. [ABSTRACT FROM AUTHOR]

The impact of the collisional warm-rain microphysical processes on the polarimetric radar variables is quantified using a coupled microphysics-electromagnetic scattering model. A one-dimensional bin-microphysical rain shaft model that resolves explicitly the evolution of the drop size distribution (DSD) under the influence of collisional coalescence and breakup, drop settling, and aerodynamic breakup is coupled with electromagnetic scattering calculations that simulate vertical profiles of the polarimetric radar variables: reflectivity factor at horizontal polarization Z H, differential reflectivity ZDR, and specific differential phase KDP. The polarimetric radar fingerprint of each individual microphysical process is quantified as a function of the shape of the initial DSD and for different values of nominal rainfall rate. Results indicate that individual microphysical processes (collisional processes, evaporation) display a distinctive signature and evolve within specific areas of Z H- ZDR and ZDR- KDP space. Furthermore, a comparison of the resulting simulated vertical profiles of the polarimetric variables with radar and disdrometer observations suggests that bin-microphysical parameterizations of drop breakup most frequently used are overly aggressive for the largest rainfall rates, resulting in very 'tropical' DSDs heavily skewed toward smaller drops. [ABSTRACT FROM AUTHOR]

The objective of this paper is to investigate long-term inter-annual, seasonal, and diurnal rainfall characteristics in the Southeastern United States. In order to capture precipitation features at high resolution, we use precipitation estimates from the Tropical Rainfall Measuring Mission (TRMM); the TRMM Precipitation Radar (TPR 2A25: 0.05°×0.05°/daily) and the TRMM Multi-satellite Precipitation Analysis (TMPA 3B42: 0.25°×0.25°/3-h) datasets to create a 13-year rainfall climatology. The higher resolution climatology (2A25) displays a greater ability to capture more localized landform precipitation features when compared with 3B42. On an annual basis, the Southeastern US is characterized by a succession of cold and warm precipitation regimes. The cold season is characterized by higher rain intensity West of 82°W (roughly Atlanta, GA) and the warm season is characterized by higher rain intensity over the coastal areas. The cold/warm rainfall regime duality is modulated by local topographic characteristics that prevail along a W–E direction. During the cold season, the diurnal cycle of precipitation is characterized by a quasi-constant repartition of rain events throughout the day and an absence of land/ocean contrast. On the contrary for summertime there is a strong land/ocean signature with a predominance of late morning/early afternoon (12:00–15:00LST) rainfall over ocean and afternoon/early evening (15:00–18:00LST) precipitation events over land that account for more than 25% of the daily events along the coasts. Differences are observed for the Florida peninsula, where the diurnal cycle displays an afternoon maximum of variable intensity due to sea breeze effects regardless of the season. [ABSTRACT FROM AUTHOR]

The objective of this paper is to characterize the precipitation amounts originating from tropical cyclones (TCs) in the southeastern United States during the tropical storm season from June to November. Using 12 years of precipitation data from the Tropical Rainfall Measurement Mission (TRMM), the authors estimate the TC contribution on the seasonal, interannual, and monthly precipitation budget using TC information derived from the International Best Track Archive for Climate Stewardship (IBTrACS). Results derived from the TRMM Multisatellite Precipitation Analysis (TMPA) 3B42 showed that TCs accounted for about 7% of the seasonal precipitation total from 1998 to 2009. Rainfall attributable to TCs was found to contribute as much as 8%-12% for inland areas located between 150 and 300 km from the coast and up to 15%-20% for coastal areas from Louisiana to the Florida Panhandle, southern Florida, and coastal Carolinas. The interannual contribution varied from 1.3% to 13.8% for the period 1998-2009 and depended on the TC seasonal activity, TC intensity, and TC paths as they traveled inland. For TCs making landfall, the rainfall contribution could be locally above 40% and, on a monthly basis, TCs contributed as much as 20% of September rainfall. The probability density functions of rainfall attributable to tropical cyclones showed that the percentage of rainfall associated with TC over land increased with increasing rain intensity and represent about 20% of heavy rainfall (>20 mm h−1), while TCs account for less than 5% of all seasonal precipitation events. [ABSTRACT FROM AUTHOR]

The objective of this study is to evaluate the impact of a new parameterization of drop-drop collision outcomes based on the relationship between Weber number and drop diameter ratios on the dynamical simulation of raindrop size distributions. Results of the simulations with the new parameterization are compared with those of the classical parameterizations. Comparison with previous results indicates on average an increase of 70% in the drop number concentration and a 15% decrease in rain intensity for the equilibrium drop size distribution (DSD). Furthermore, the drop bounce process is parameterized as a function of drop size based on laboratory experiments for the first time in a microphysical model. Numerical results indicate that drop bounce has a strong influence on the equilibrium DSD, in particular for very small drops (<0.5 mm), leading to an increase of up to 150% in the small drop number concentration (left-hand side of the DSD) when compared to previous modeling results without accounting for bounce effects. [ABSTRACT FROM AUTHOR]

Summary: A new rain gauge network was installed in the Great Smoky Mountains National Park (GSMNP) in the Southern Appalachians since 2007 to investigate the space–time distribution of precipitation in the inner mountain region. Exploratory Intense Observing Periods (IOPs) have been conducted in the summer and fall seasons to devise optimal long-term monitoring strategies, and Micro Rain Radars (MRR) were deployed twice in July/August and October/November 2008 at a mountain ridge location and a nearby valley. Rain gauge and MRR observations were analyzed to characterize seasonal (summer/fall) and orographic (valley/ridge) precipitation features. The data show that summer precipitation is characterized by large event-to-event variability including both stratiform and convective properties. During fall, stratiform precipitation dominates and rainfall is two times more frequent at the ridge than in the valley, corresponding to a 100% increase in cumulative rainfall at high elevation. For concurrent rain events, the orographic enhancement effect is on the order of 60%. Evidence of a seasonal signature in the drop size distribution (DSD) was found with significantly heavier tails (larger raindrops) for summer DSDs at higher elevations, whereas no significant differences were observed between ridge and valley locations during fall deployment. However, physically-based modeling experiments suggest that there are inconsistencies between the reflectivity profiles and MRR DSD estimates when large raindrop sizes are present. The number of very small drops is very high (up to two orders of magnitude) at high elevations as compared to the typical values in the literature, which cannot be explained only by fog and drizzle and suggest an important role for mixed phase processes in determining the shape of the DSD below the brightband. Because numerical modeling experiments show that coalescence is the dominant microphysical mechanism for DSD evolution for the relatively low to moderate observed rain rates characteristic of mountainous regions, it is therefore critical to clarify the shape and parameters that characterize the left-hand side of the DSD in mountainous regions. Finally, whereas low cost Micro Rain Radars (MRR) were found particularly useful for qualitative description of precipitation events and to identify rain/snow melting conditions, when compared against collocated rain gauges, MRR Quantitative Precipitation Estimation (QPE) is not reliable. Place-based calibration and reliance upon physically-based QPE retrieval algorithms can improve their utility. [Copyright &y& Elsevier]

The objective of this study is to characterize the signature of dynamical microphysical processes on reflectivity–rainfall (Z–R) relationships used for radar rainfall estimation. For this purpose, a bin model with explicit microphysics was used to perform a sensitivity analysis of the shape parameters of the drop size distribution (DSD) as a function of time and rainfall regime. Simulations show that coalescence is the dominant microphysical process for low to moderate rain intensity regimes (R < 20 mm h-1) and that the rain rate in this regime is strongly dependent on the spectral properties of the DSD (i.e., the shape). The time to equilibrium for light rainfall is at least twice as long as in the case of heavy rainfall (1 h for stratiform vis-à-vis 30 min for thunderstorms). For high-intensity rainfall (R > 20 mm h-1), collision–breakup dynamics dominate the evolution of the raindrop spectra. The time-dependent Z–R relationships produced by the model converge to a universal Z–R relationship for heavy intensity rainfall (A = 1257; b ∼ 1) centered on the region of Z–R space defined by the ensemble of over 100 empirical Z–R relationships. Given the intrinsically transient nature of the DSD for light rainfall, it is proposed that the vertical raindrop spectra and corresponding rain rates should be modeled explicitly by a microphysical model. A demonstration using a multicolumn simulation of a Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) overpass over Darwin for a stratiform event during the Tropical Warm Pool–International Cloud Experiment (TWP-ICE) is presented. [ABSTRACT FROM AUTHOR]

A model of rain shaft microphysics that solves the stochastic advection–coalescence–breakup equation in an atmospheric column was used to simulate the evolution of a stratiform rainfall event during the Tropical Warm Pool-International Cloud Experiment (TWP-ICE) in Darwin, Australia. For the first time, a dynamic simulation of the evolution of the drop spectra within a one-dimensional rain shaft is performed using realistic boundary conditions retrieved from real rain events. Droplet size distribution (DSD) retrieved from vertically pointing radar (VPR) measurements are sequentially imposed at the top of the rain shaft as boundary conditions to emulate a realistic rain event. Time series of model profiles of integral parameters such as reflectivity, rain rate, and liquid water content were subsequently compared with estimates retrieved from vertically pointing radars and Joss–Waldvogel disdrometer (JWD) observations. Results obtained are within the VPR retrieval uncertainty estimates. Besides evaluating the model’s ability to capture the dynamical evolution of the DSD within the rain shaft, a case study was conducted to assess the potential use of the model as a physically based interpolator to improve radar retrieval at low levels in the atmosphere. Numerical results showed that relative improvements on the order of 90% in the estimation of rain rate and liquid water content can be achieved close to the ground where the VPR estimates are less reliable. These findings raise important questions with regard to the importance of bin resolution and the lack of sensitivity for small raindrop size (<0.03 cm) in the interpretation of JWD data, and the implications of using disdrometer data to calibrate radar algorithms. [ABSTRACT FROM AUTHOR]

Raindrop collision and breakup is a stochastic process that affects the evolution of drop size distributions (DSDs) in precipitating clouds. Low and List have remained the obligatory reference on this matter for almost three decades. Based on a limited number of drop sizes (10), Low and List proposed generalized parameterizations of collisional breakup across the raindrop spectra that are standard building blocks for numerical models of rainfall microphysics. Here, recent laboratory experiments of drop collision at NASA’s Wallops Island Facility (NWIF) using updated high-speed imaging technology with the objective of assessing the generality of Low and List are reported. The experimental fragment size distributions (FSDs) for the collision of selected drop pairs were evaluated against explicit simulations using a dynamical microphysics model (Prat and Barros, with parameterizations based on Low and List updated by McFarquhar). One-to-one comparison of the FSDs shows similar distributions; however, the model was found to underestimate the fragment numbers observed in the smallest diameter range (e.g., D < 0.2 mm), and to overestimate the number of fragments produced when small drops (diameter DS ≥ 1mm) and large drops (diameter DL ≥ 3mm) collide. This effect is particularly large for fragments in the 0.5–1.0-mm range, and more so for filament breakup (the most frequent type of breakup observed in laboratory conditions), reflecting up to 30% uncertainty in the left-hand side of the FSD (i.e., the submillimeter range). For coalescence, the NWIF experiments confirmed the drop collision energy cutoff (ET) estimated by Low and List (i.e., ET > 5.0 μJ). Finally, the digital imagery of the laboratory experiments was analyzed to determine the characteristic time necessary to reach stability in relevant statistical properties. The results indicate that the temporal separation between particle (i.e., single hydrometeor) and population behavior, that is, the characteristic time scale to reach homogeneity in the NWIF raindrop populations, is 160 ms, which provides a lower bound to the governing time scale in population-based microphysical models. [ABSTRACT FROM AUTHOR]

Precipitation estimation by weather radars in Alaska is challenging. In this study, we investigate National Weather Service (NWS) precipitation products that are produced from the seven NEXRAD radar sites in Alaska. The NWS precipitation processing subsystem generates stages of data at each NEXRAD site which are then input to the weather forecast office to generate a regionwide precipitation product. Data from the NEXRAD sites and the operational rain gauges in the weather forecast region are used to produce this regionwide product that is then sent to the National Centers for Environmental Prediction (NCEP) to be included in the NCEP Stage IV distribution. The NCEP Stage IV product for Alaska has been available since 2017. We use the United States Climate Reference Network (USCRN) data from Alaska to compare to the NCEP Stage IV data. Given that the USCRN can be used in the production of the NCEP Stage IV data for Alaska, we also used the NEXRAD Digital Precipitation Array (DPA) that is generated at the site for comparison of the radar-only products. Comparing the NEXRAD-based data from Alaska to the USCRN gauge estimates using the USCRN site information on air temperature, we are able to condition the analysis based on the hourly or 6-hourly average air temperature. The estimates in the frozen phase of precipitation largely underestimate as compared to the gauge, and the correlation is low with larger errors as compared to other phases of precipitation. In the mixed phase the underestimation of precipitation improves, but the correlation is still low with relatively large errors as compared to the rain phases of precipitation. The difficulties in precipitation estimation in cold temperatures are well known and we show the evaluation for the NCEP Stage IV regional data for Alaska and the NEXRAD site specific Digital Precipitation Array (DPA) data. Results show the challenges of estimating mixed-phase and frozen precipitation. However, the DPA data shows somewhat better performance in the mixed precipitation phase, which suggests that the NWS Precipitation Processing Subsystem (PPS) is tuned to the climatology as it relates to precipitation in Alaska. [ABSTRACT FROM AUTHOR]

The focus of this paper is on the numerical solution of the stochastic collection equation–stochastic breakup equation (SCE–SBE) describing the evolution of raindrop spectra in warm rain. The drop size distribution (DSD) is discretized using the fixed-pivot scheme proposed by Kumar and Ramkrishna, and new discrete equations for solving collision breakup are presented. The model is evaluated using established coalescence and breakup parameterizations (kernels) available in the literature, and in that regard this paper provides a substantial review of the relevant science. The challenges posed by the need to achieve stable and accurate numerical solutions of the SCE–SBE are examined in detail. In particular, this paper focuses on the impact of varying the shape of the initial DSD on the equilibrium solution of the SCE–SBE for a wide range of rain rates and breakup kernels. The results show that, although there is no dependence of the equilibrium DSD on initial conditions for the same rain rate and breakup kernel, there is large variation in the time that it takes to reach steady state. This result suggests that, in coupled simulations of in-cloud motions and microphysics and for short time scales (<30 min) for which transient conditions prevail, the equilibrium DSD may not be attainable except for very heavy rainfall. Furthermore, simulations for the same initial conditions show a strong dependence of the dynamic evolution of the DSD on the breakup parameterization. The implication of this result is that, before the debate on the uniqueness of the shape of the equilibrium DSD can be settled, there is critical need for fundamental research including laboratory experiments to improve understanding of collisional mechanisms in DSD evolution. [ABSTRACT FROM AUTHOR]

Abstract: A study was performed that utilizes the quadrature method of moments (QMOM) to model the transient spatial evolution of the floc size in a heterogeneous turbulent stirred reactor. The QMOM approach was combined with a commercial computational fluid dynamics (CFD) code (PHOENICS), which was used to simulate the turbulent flow and transport of these aggregates in the reactor. The CFD/QMOM model was applied to a 28l square reactor containing an axial flow impeller and 100mg/l concentration of 1 m nominal clay particles. Simulations were performed for different average characteristic velocity gradients (, and 150s). The average floc size and growth rate were compared with experimental measurements performed in the bulk region and the impeller discharge region. The CFD/QMOM results confirmed the experimentally measured spatial heterogeneity in the floc size and growth rate. In addition, the model predicts spatial variations in the aggregation and breakup rates. Finally, the model also predicts that the transport of flocs into the high shear impeller discharge zone was responsible for the transient evolution of the average floc size curve displaying a maximum before decreasing to a steady-state floc size. [Copyright &y& Elsevier]

There is growing interest in combining microphysical models and polarimetric radar observations to improve our understanding of storms and precipitation. Mapping model-predicted variables into the radar observational space necessitates a forward operator, which requires assumptions that introduce uncertainties into model–observation comparisons. These include uncertainties arising from the microphysics scheme a priori assumptions of a fixed drop size distribution (DSD) functional form, whereas natural DSDs display far greater variability. To address this concern, this study presents a moment-based polarimetric radar forward operator with no fundamental restrictions on the DSD form by linking radar observables to integrated DSD moments. The forward operator is built upon a dataset of >200 million realistic DSDs from one-dimensional bin microphysical rain-shaft simulations, and surface disdrometer measurements from around the world. This allows for a robust statistical assessment of forward operator uncertainty and quantification of the relationship between polarimetric radar observables and DSD moments. Comparison of "truth" and forward-simulated vertical profiles of the polarimetric radar variables are shown for bin simulations using a variety of moment combinations. Higher-order moments (especially those optimized for use with the polarimetric radar variables: the sixth and ninth) perform better than the lower-order moments (zeroth and third) typically predicted by many bulk microphysics schemes. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented concerning the description of bag break-up of drop size distribution in the 2009 issue.

The main challenge of evaluating droughts in the context of climate change and linking these droughts to adverse societal outcomes is a lack of a uniform definition that identifies drought conditions at a location and time. The U.S. Drought Monitor (USDM), created in 1999, is a well‐established composite index that combines drought indicators across the hydrological cycle (i.e., meteorological to hydrological) with information from local experts. This makes the USDM one of the most holistic measures for evaluating past drought conditions across the United States. In this study, the USDM was used to define drought events as consecutive periods in time where the USDM status met or exceeded D1 conditions over the past 20 years. This analysis was applied to 5 km grid cells covering the U.S. and Puerto Rico to characterize the frequency, duration, and intensification rates of drought, and the timing of onset, amelioration, and other measures for every drought event on record. Results from this analysis revealed stark contrasts in the evolution of drought across the United States. Over the western United States, droughts evolved much slower, resulting in longer‐lasting but fewer droughts. The eastern United States experienced more frequent, shorter‐duration events. Given the slower evolution from onset to drought peak, flash droughts, which made up 9.8% of all droughts, were less common across the western United States, with a greater frequency over the southern United States. The most severe drought event on record was the 2012 drought, when more than 21% of the United States experienced its largest number of weeks at or above extreme (D3) drought conditions. The availability of historical drought events would support future societal impacts studies relating drought to adverse outcomes and aid in the evaluation of mitigation strategies by providing a dataset to local decision makers to compare and evaluate past droughts. [ABSTRACT FROM AUTHOR]

In the atmosphere, microphysics refers to the microscale processes that affect cloud and precipitation particles and is a key linkage among the various components of Earth's atmospheric water and energy cycles. The representation of microphysical processes in models continues to pose a major challenge leading to uncertainty in numerical weather forecasts and climate simulations. In this paper, the problem of treating microphysics in models is divided into two parts: (i) how to represent the population of cloud and precipitation particles, given the impossibility of simulating all particles individually within a cloud, and (ii) uncertainties in the microphysical process rates owing to fundamental gaps in knowledge of cloud physics. The recently developed Lagrangian particle‐based method is advocated as a way to address several conceptual and practical challenges of representing particle populations using traditional bulk and bin microphysics parameterization schemes. For addressing critical gaps in cloud physics knowledge, sustained investment for observational advances from laboratory experiments, new probe development, and next‐generation instruments in space is needed. Greater emphasis on laboratory work, which has apparently declined over the past several decades relative to other areas of cloud physics research, is argued to be an essential ingredient for improving process‐level understanding. More systematic use of natural cloud and precipitation observations to constrain microphysics schemes is also advocated. Because it is generally difficult to quantify individual microphysical process rates from these observations directly, this presents an inverse problem that can be viewed from the standpoint of Bayesian statistics. Following this idea, a probabilistic framework is proposed that combines elements from statistical and physical modeling. Besides providing rigorous constraint of schemes, there is an added benefit of quantifying uncertainty systematically. Finally, a broader hierarchical approach is proposed to accelerate improvements in microphysics schemes, leveraging the advances described in this paper related to process modeling (using Lagrangian particle‐based schemes), laboratory experimentation, cloud and precipitation observations, and statistical methods. Plain Language Summary: In the atmosphere, microphysics—the small‐scale processes affecting cloud and precipitation particles such as their growth by condensation, evaporation, and melting—is a critical part of Earth's weather and climate. Because it is impossible to simulate every cloud particle individually owing to their sheer number within even a small cloud, atmospheric models have to represent the evolution of particle populations statistically. There are critical gaps in knowledge of the microphysical processes that act on particles, especially for atmospheric ice particles because of their wide variety and intricacy of their shapes. The difficulty of representing cloud and precipitation particle populations and knowledge gaps in cloud processes both introduce important uncertainties into models that translate into uncertainty in weather forecasts and climate simulations, including climate change assessments. We discuss several specific challenges related to these problems. To improve how cloud and precipitation particle populations are represented, we advocate a "particle‐based" approach that addresses several limitations of traditional approaches and has recently gained traction as a tool for cloud modeling. Advances in observations, including laboratory studies, are argued to be essential for addressing gaps in knowledge of microphysical processes. We also advocate using statistical modeling tools to improve how these observations are used to constrain model microphysics. Finally, we discuss a hierarchical approach that combines the various pieces discussed in this article, providing a possible blueprint for accelerating progress in how microphysics is represented in cloud, weather, and climate models. Key Points: Microphysics is an important component of weather and climate models, but its representation in current models is highly uncertainTwo critical challenges are identified: representing cloud and precipitation particle populations and knowledge gaps in cloud physicsA possible blueprint for addressing these challenges is proposed to accelerate progress in improving microphysics schemes [ABSTRACT FROM AUTHOR]

Observationally informed development of a new framework for bulk rain microphysics, the Bayesian Observationally Constrained Statistical–Physical Scheme (BOSS; described in Part I of this study), is demonstrated. This scheme's development is motivated by large uncertainties in cloud and weather simulations associated with approximations and assumptions in existing microphysics schemes. Here, a proof-of-concept study is presented using a Markov chain Monte Carlo sampling algorithm with BOSS to probabilistically estimate microphysical process rates and parameters directly from a set of synthetically generated rain observations. The framework utilized is an idealized steady-state one-dimensional column rainshaft model with specified column-top rain properties and a fixed thermodynamical profile. Different configurations of BOSS—flexibility being a key feature of this approach—are constrained via synthetic observations generated from a traditional three-moment bulk microphysics scheme. The ability to retrieve correct parameter values when the true parameter values are known is illustrated. For cases when there is no set of true parameter values, the accuracy of configurations of BOSS that have different levels of complexity is compared. It is found that addition of the sixth moment as a prognostic variable improves prediction of the third moment (proportional to bulk rain mass) and rain rate. In contrast, increasing process rate formulation complexity by adding more power terms has little benefit—a result that is explained using further-idealized experiments. BOSS rainshaft simulations are shown to well estimate the true process rates from constraint by bulk rain observations, with the additional benefit of rigorously quantified uncertainty of these estimates. [ABSTRACT FROM AUTHOR]

A new framework is proposed for the bulk parameterization of rain microphysics: the Bayesian Observationally Constrained Statistical–Physical Scheme (BOSS). It is designed to facilitate direct constraint by observations using Bayesian inference. BOSS combines existing process-level microphysical knowledge with flexible process rate formulations and parameters constrained by observations within a Bayesian framework. Using a raindrop size distribution (DSD) normalization method that relates DSD moments to one another via generalized power series, generalized multivariate power expressions are derived for the microphysical process rates as functions of a set of prognostic DSD moments. The scheme is flexible and can utilize any number and combination of prognostic moments and any number of terms in the process rate formulations. This means that both uncertainty in parameter values and structural uncertainty associated with the process rate formulations can be investigated systematically, which is not possible using traditional schemes. In this paper, BOSS is compared to two- and three-moment versions of a traditional bulk rain microphysics scheme (denoted as MORR). It is shown that some process formulations in MORR are analytically equivalent to the generalized power expressions in BOSS using one or two terms, while others are not. BOSS is able to replicate the behavior of MORR in idealized one-dimensional rainshaft tests, but with a much more flexible and systematic design. Part II of this study describes the application of BOSS to derive rain microphysical process rates and posterior parameter distributions in Bayesian experiments using Markov chain Monte Carlo sampling constrained by synthetic observations. [ABSTRACT FROM AUTHOR]

A general drop size distribution (DSD) normalization method is formulated in terms of generalized power series relating any DSD moment to any number and combination of reference moments. This provides a consistent framework for comparing the variability of normalized DSD moments using different sets of reference moments, with no explicit assumptions about the DSD functional form (e.g., gamma). It also provides a method to derive any unknown moment plus an estimate of its uncertainty from one or more known moments, which is relevant to remote sensing retrievals and bulk microphysics schemes in weather and climate models. The approach is applied to a large dataset of disdrometer-observed and bin microphysics-modeled DSDs. As expected, the spread of normalized moments decreases as the number of reference moments is increased, quantified by the logarithmic standard deviation of the normalized moments, σ. Averaging σ for all combinations of reference moments and normalized moments of integer order 0–10, 42.9%, 81.3%, 93.7%, and 96.9% of spread are accounted for applying one-, two-, three-, and four-moment normalizations, respectively. Thus, DSDs can be well characterized overall using three reference moments, whereas adding a fourth reference moment contributes little independent information. The spread of disdrometer-observed DSD moments from uncertainty associated with drop count statistics generally lies between values of σ using two- and three-moment normalizations. However, this uncertainty has little impact on the derived DSD scaling relationships or σ when considered. [ABSTRACT FROM AUTHOR]

A new technique for merging radar precipitation estimates and rain gauge data is developed and evaluated to improve multisensor quantitative precipitation estimation (QPE), in particular, of heavy-to-extreme precipitation. Unlike the conventional cokriging methods which are susceptible to conditional bias (CB), the proposed technique, referred to herein as conditional bias-penalized cokriging (CBPCK), explicitly minimizes Type-II CB for improved quantitative estimation of heavy-to-extreme precipitation. CBPCK is a bivariate version of extended conditional bias-penalized kriging (ECBPK) developed for gauge-only analysis. To evaluate CBPCK, cross validation and visual examination are carried out using multi-year hourly radar and gauge data in the North Central Texas region in which CBPCK is compared with the variant of the ordinary cokriging (OCK) algorithm used operationally in the National Weather Service Multisensor Precipitation Estimator. The results show that CBPCK significantly reduces Type-II CB for estimation of heavy-to-extreme precipitation, and that the margin of improvement over OCK is larger in areas of higher fractional coverage (FC) of precipitation. When FC > 0.9 and hourly gauge precipitation is > 60 mm, the reduction in root mean squared error (RMSE) by CBPCK over radar-only (RO) is about 12 mm while the reduction in RMSE by OCK over RO is about 7 mm. CBPCK may be used in real-time analysis or in reanalysis of multisensor precipitation for which accurate estimation of heavy-to-extreme precipitation is of particular importance. [ABSTRACT FROM AUTHOR]

A new retrospective satellite-based precipitation dataset is constructed as a climate data record for hydrological and climate studies. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) provides daily and 0.25° rainfall estimates for the latitude band 60°S-60°N for the period of 1 January 1983 to 31 December 2012 (delayed present). PERSIANN-CDR is aimed at addressing the need for a consistent, long-term, high-resolution, and global precipitation dataset for studying the changes and trends in daily precipitation, especially extreme precipitation events, due to climate change and natural variability. PERSIANN-CDR is generated from the PERSIANN algorithm using GridSat-Bl infrared data. It is adjusted using the Global Precipitation Climatology Project (GPCP) monthly product to maintain consistency of the two datasets at 2.5° monthly scale throughout the entire record. Three case studies for testing the efficacy of the dataset against available observations and satellite products are reported. The verification study over Hurricane Katrina (2005) shows that PERSIANN-CDR has good agreement with the stage IV radar data, noting that PERSIANN-CDR has more complete spatial coverage than the radar data. In addition, the comparison of PERSIANN-CDR against gauge observations during the 1986 Sydney flood in Australia reaffirms the capability of PERSIANN-CDR to provide reasonably accurate rainfall estimates. Moreover, the probability density function (PDF) of PERSIANN-CDR over the contiguous United States exhibits good agreement with the PDFs of the Climate Prediction Center (CPC) gridded gauge data and the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) product. The results indicate high potential for using PERSIANN-CDR for long-term hydroclimate studies in regional and global scales. [ABSTRACT FROM AUTHOR]